Multi-scale directional-filtering-based method for follicular lymphoma grading

Follicular lymphoma (FL) is a group of malignancies of lymphocyte origin that arise from lymph nodes, spleen, and bone marrow in the lymphatic system. It is the second most common non-Hodgkins lymphoma. Characteristic of FL is the presence of follicle center B cells consisting of centrocytes and centroblasts. Typically, FL images are graded by an expert manually counting the centroblasts in an image. This is time consuming. In this paper, we present a novel multi-scale directional filtering scheme and utilize it to classify FL images into different grades. Instead of counting the centroblasts individually, we classify the texture formed by centroblasts. We apply our multi-scale directional filtering scheme in two scales and along eight orientations, and use the mean and the standard deviation of each filter output as feature parameters. For classification, we use support vector machines with the radial basis function kernel. We map the features into two dimensions using linear discriminant analysis prior to classification. Experimental results are presented.     

机载激光雷达数据中道路中线的多尺度提取方法

机载激光雷达(LIDAR)技术的出现为道路特征的获取提供了新的途径.在分析现有道路提取现状的基础上,针对激光雷达数据的特点以及单一尺度下道路中线提取方法的不足,提出一种基于多尺度追踪的道路中线提取方法.该方法首先采用逐步约束的方法进行道路激光点的提取,包括高程约束、强度约束以及区域点密度和区域面积的约束等;然后基于道路点云生成的不同尺度距离影像的形态学细化结果,采用多尺度追踪的方法实现道路中线的提取,其中多尺度追踪方法由大尺度道路中线的迭代追踪以及小尺度道路中线的启发式追踪两部分组成.最后采用实地数据进行验证,结果表明:该方法能有效地从LIDAR点云中提取道路中线信息,并具有较好的精度.     

A novel method for the automatic grading of retinal vessel tortuosity

Tortuosity is among the first alterations in the retinal vessel network to appear in many retinopathies, such as those due to hypertension. An automatic evaluation of retinal vessel tortuosity would help the early detection of such retinopathies. Quite a few techniques for tortuosity measurement and classification have been proposed, but they do not always match the clinical concept of tortuosity. This justifies the need for a new definition, able to express in mathematical terms the tortuosity as perceived by ophthalmologists. We propose here a new algorithm for the evaluation of tortuosity in vessels recognized in digital fundus images. It is based on partitioning each vessel in segments of constant-sign curvature and then combining together each evaluation of such segments and their number. The algorithm has been compared with other available tortuosity measures on a set of 30 arteries and one of 30 veins from 60 different images. These vessels had been preliminarily ordered by a retina specialist by increasing perceived tortuosity. The proposed algorithm proved to be the best one in matching the clinically perceived vessel tortuosity.     

A subject-independent method for automatically grading electromyographic features during a fatiguing contraction

Many studies have attempted to monitor fatigue from electromyogram (EMG) signals. However, fatigue affects EMG in a subject-specific manner. We present here a subject-independent framework for monitoring the changes in EMG features that accompany muscle fatigue based on principal component analysis and factor analysis. The proposed framework is based on several time- and frequency-domain features, unlike most of the existing work, which is based on two to three features. Results show that latent factors obtained from factor analysis on these features provide a robust and unified framework. This framework learns a model from EMG signals of multiple subjects, that form a reference group, and monitors the changes in EMG features during a sustained submaximal contraction on a test subject on a scale from zero to one. The framework was tested on EMG signals collected from 12 muscles of eight healthy subjects. The distribution of factor scores of the test subject, when mapped onto the framework was similar for both the subject-specific and subject-independent cases.     

Using adaptive neuro-fuzzy inference systems for the detection of centroblasts in microscopic images of follicular lymphoma

In this paper, a complete methodology for automatic detection of centroblasts (CBs) in microscopic images acquired from tissue biopsies of follicular lymphoma is presented. In the proposed method, tissue sections are sliced at a low thickness level, around 1–1.5 \(\upmu \)m, which provides a more detailed depiction of the nuclei and other textural information. Initially, images are segmented into their basic cytological components, i.e., blood cells, nuclei and extra-cellular material, and then a novel touching-cell splitting algorithm is applied using a Gaussian mixture model and expectation–maximization algorithm. Additionally, a morphological and textural analysis of CBs is applied in order to extract various features related to their nuclei, nucleoli and cytoplasm. In the final step, a novel classification scheme is proposed based on adaptive neuro-fuzzy inference systems to classify the candidate cells. The methodology yielded promising results with an average detection rate of 90.35 %.     

Multi-scale detector optimized for small target

The effectiveness of deep learning networks in detecting small objects is limited,thereby posing challenges in ad-dressing practical object detection tasks.In this research,we propose a small object detection model that operates at multiple scales.The model incorporates a multi-level bidirectional pyramid structure,which integrates deep and shal-low networks to simultaneously preserve intricate local details and augment global features.Moreover,a dedicated multi-scale detection head is integrated into the model,specifically designed to capture crucial information pertaining to small objects.Through comprehensive experimentation,we have achieved promising results,wherein our proposed model exhibits a mean average precision(mAP)that surpasses that of the well-established you only look once version 7(YOLOv7)model by 1.1％.These findings validate the improved performance of our model in both conventional and small object detection scenarios.     

用于纹理检索的多尺度复杂性纹理描述子

提出了基于复杂性度量和图像多尺度分析的纹理特征提取方法,提取后的特征用于纹理检索.使用的复杂性度量为一维和二维CO复杂性.采用的图像多尺度分析方法有金字塔分解、小波分解(Haar小波与Db2小波)和二维经验模式分解.实验结果表明:提出的基于Db2小波分解的一维和二维CO复杂性特征是适于纹理检索的特征.它们均取得了和Ga...     

多尺度语义信息融合的目标检测

针对当前目标检测算法对小目标及密集目标检测效果差的问题,该文在融合多种特征和增强浅层特征表征能力的基础上提出了浅层特征增强网络(SEFN),首先将特征提取网络VGG16中Conv4_3层和Conv5_3层提取的特征进行融合形成基础融合特征;然后将基础融合特征输入到小型的多尺度语义信息融合模块中,得到具有丰富上下文信息和空间细节信息的语义特征,同时把语义特征和基础融合特征经过特征重利用模块获得浅层增强特征;最后基于浅层增强特征进行一系列卷积获取多个不同尺度的特征,并输入各检测分支进行检测,利用非极大值抑制算法实现最终的检测结果.在PASCAL VOC2007和MS COCO2014数据集上进行测试,模型的平均精度均值分别为81.2％和33.7％,相对于经典的单极多盒检测器(SSD)算法,分别提高了2.7％和4.9％;此外,该文方法在检测小目标和密集目标场景上,检测精度和召回率都有显著提升.实验结果表明该文算法采用特征金字塔结构增强了浅层特征的语义信息,并利用特征重利用模块有效保留了浅层的细节信息用于检测,增强了模型对小目标和密集目标的检测效果.